Extrusion-based 3D bioprinting (EBBP) prints tissues, including nerve guide conduits, bone tissue engineering, skin tissue repair, cartilage repair, and muscle repair. The EBBP demands optimized parameters for obtaining good printability and cell viability. However, finding optimal process parameters is always essential for the researcher. The biological, mechanical, and rheological parameters all together need to be evaluated to enhance the printability of tissue. A degree of simplicity may be required to interpret each parameter's effect. However, overcoming complexity with a multiparameter is quite tricky through conventional methods. It can be overcome with the implementation of machine learning. This article concisely delineates the application of machine learning algorithms for modeling printability as a function of influential parameters was elaborately discussed. Additionally, indispensable challenges and futuristic aspects were briefed concerning tissue regeneration applications.Le texte complet de cet article est disponible en PDF.
Keywords : Bioprinting, Printability, Hydrogels, Crosslinking, Machine learning